Transactions of the American Fisheries Society

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Bioelectrical Impedance Analysis to Estimate Lipid Content in Atlantic Salmon Parr as Influenced by Temperature, PIT Tags, and Instrument Precision and Application in Field Studies a

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Sherr Vue , Kurt M. Samways & Richard A. Cunjak

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Canadian Rivers Institute and Department of Biology, University of New Brunswick, 10 Bailey Drive, Fredericton, New Brunswick, E3B 5A3, Canada Published online: 18 Feb 2015.

Click for updates To cite this article: Sherr Vue, Kurt M. Samways & Richard A. Cunjak (2015) Bioelectrical Impedance Analysis to Estimate Lipid Content in Atlantic Salmon Parr as Influenced by Temperature, PIT Tags, and Instrument Precision and Application in Field Studies, Transactions of the American Fisheries Society, 144:2, 235-245, DOI: 10.1080/00028487.2014.972576 To link to this article: http://dx.doi.org/10.1080/00028487.2014.972576

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Transactions of the American Fisheries Society 144:235–245, 2015 Ó American Fisheries Society 2015 ISSN: 0002-8487 print / 1548-8659 online DOI: 10.1080/00028487.2014.972576

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Bioelectrical Impedance Analysis to Estimate Lipid Content in Atlantic Salmon Parr as Influenced by Temperature, PIT Tags, and Instrument Precision and Application in Field Studies

Downloaded by [University of New Brunswick] at 12:48 20 February 2015

Sherr Vue,* Kurt M. Samways, and Richard A. Cunjak Canadian Rivers Institute and Department of Biology, University of New Brunswick, 10 Bailey Drive, Fredericton, New Brunswick, E3B 5A3, Canada

Abstract The objective of this study was to investigate the effectiveness of bioelectrical impedance analysis (BIA) in field studies monitoring Atlantic Salmon Salmo salar parr, as related to temperature corrections, instrument precision, and passive integrated transponder (PIT) tags. Currently, BIA studies are restricted to laboratory settings where water temperature is controlled to decrease error in BIA predictions caused by fish body temperature. We compared models of predicted total and percent lipids with and without temperature corrections and found that temperature corrections reduced error caused by temperature. Without temperature corrections, an 8! C increase in temperature increased the predicted total lipids by 55%. After temperature corrections were added, the predicted total lipid only increased by 2.55%. Repeated measurements were collected on 40 salmon parr (56–115 mm FL) in four separate time trials (1 min, 1.5 h, 3 h, and 6 h), and we found that lipid content predictions between measurements were not significantly different; however, the variability within longer time trials was moderate (6.43% error). No significant differences were found in the predicted lipid value before or after PIT tags were removed from the body cavity, suggesting PIT tags do not affect BIA readings. On average, the difference between predicted total lipids after tag removal was 0.0023 and 0.02 g for 12.5-mm and 22-mm PIT tags, respectively. We also observed that increases in fish body temperature caused by handing resulted in increased variability in BIA estimates, indicating the need for temperature corrections.

Traditional lipid analysis of fish tissues requires lethal sampling followed by extraction of lipids using laboratory techniques (e.g., Bligh and Dyer 1959). Such methods preclude the ability to monitor temporal changes in lipid content of fish killed during sampling. Estimating lipid content

using bioelectrical impedance analysis (BIA) avoids lethal sampling by correlating electrical properties in body tissues to lipid content. In humans, BIA is widely used in medical clinics to estimate lipid content using noninvasive electrode pads (Liedtke 1997). It is also used in studies of fish biology to estimate total lipid content, dry weight, total body water, fat-free mass, total body protein, and total body ash by using modified electrodes and new predictive models (Cox and Hartman 2005; Willis and Hobday 2008; Hanson et al. 2010; Rasmussen et al. 2012). Bioelectrical impedance analysis involves the insertion of two pairs of needle electrodes into fish tissue to measure the electrical properties of resistance and reactance between two locations of a fish. It assumes that signal frequency and conductor configuration are constant and therefore impedance (Z) can be related to the volume of a substance using the following equation:

L Z Dp ; A

(1)

where Z is impedance (derived from resistance and reactance values; V), p is a specific resistivity coefficient (resistance or reactance coefficient) for the respective substance, L is the conductor length (detector length), and A is the cross-section area (Hoffer et al. 1969). This equation can then be derived by multiplying the equation by L/L and rearranged to calculate the volume of the respected substance to get the following

*Corresponding author: [email protected] Received March 10, 2014; accepted September 22, 2014

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equation:


V Dp

L2 ; Z

(2)

where V is the volume (lipid volume; mL) of the target substance. The resistivity coefficient (p) can be estimated by using regression analysis to estimate the slope to which impedance values change with substance volume. Many BIA lipid estimates are reported by weight or percentage, assuming the volume to mass ratio in lipids is 1:1, because the average specific gravity for the majority of fatty acids is 0.903 at 20! C (0.903 g/mL; Masoro 1968). There may be potential for bias in lipid estimations because the volume to mass ratio is not 1:1; however, this issue is not addressed in the literature. For estimation of lipid content (g) in fish, a multiple regression is performed using a suite of electrical values calculated from the resistance and reactance readings as the independent variables (Table 1). A linear model is then created to estimate lipid content. Resistance and reactance values from BIA readings (V) and can be calculated into series or parallel values. Values in series assume the current passing through each element is the same; values in parallel assumes that the voltage drop across each substance (cells) is the same. Resistance is the drop in voltage as an electrical current passes through an object. Lean muscle is highly conductive due to its high water and electrolyte content, which results in low resistance readings. Fat tissue and bone are poor conductors and result in high resistance readings. Reactance measures the opposition to electrical current caused by capacitance. Cell membranes act as capacitors; therefore, high reactance values indicate increased body condition and cell membrane integrity (Liedtke 1997).

Currently, BIA of fish is restricted for use in temperaturecontrolled laboratories or field studies that do not involve temporal monitoring of fish lipids. As ectothermic organisms, fish have internal body temperatures largely controlled by the environment. Temperature inversely affects resistance and reactance values derived from BIA, increases in temperature resulting in decreases in the resistance and reactance values. For example, in Chinook Salmon Oncorhynchus tshawytscha and Coho Salmon O. kisutch, resistance and reactance linearly decreased at an average rate of ¡11.65 V and ¡2.92 V, respectively, for every 1! C increase (Cox et al. 2011). This suggests that using BIA to monitor wild fish during different seasons would result in erroneous predictions. For example, developing a linear model for BIA under warm (e.g, summer) conditions would under-predict lipid content for fish experiencing colder temperatures (e.g., in winter). Hafs and Hartman (2011) controlled fish body temperature by acclimating fish to laboratory water temperatures for 12 h prior to BIA measurements. Such methods reduce measurement error in laboratory settings but preclude the application of BIA for use in field studies, where environmental temperatures can vary on diel and seasonal scales. Corrections can be applied to account for changes in BIA values caused by temperature because BIA resistance and reactance values have a linear relationship with temperature. A few studies (Cox et al. 2011; Hafs 2011:50–57; Stolarski et al. 2014) use temperature corrections to estimate total lipids and percent dry weight at a standardized temperature. The following equations are used to estimate resistance (Rm) and reactance (Xcm) at a standardized temperature (Tm):

R m D R i C a ð Tm ¡ T Þ

(3)

TABLE 1. Bioelectrical impedance analysis parameters and calculations used to develop total and percent lipid models; DL D detector length.

Parameter

Symbol

Units

Fork length Weight Resistance Rectance Resistance in series Reactance in series Resistance in parallel Reactance in parallel Capacitance Impedance in series Impedance in parallel Phase angle Standardized phase angle Body mass index

FL g r x Rs XC RP Xcp Cpf Zs Zp PA DLPA BMI

mm g Ohms Ohms Ohms Ohms Ohms Ohms Picofarads Ohms Ohms Degrees Degrees Ohms

Calculation

Measured by Quantum III Measured by Quantum III DL2/r DL2/x 2 DL /[r C (x2/r)] DL2/[x C (r2/x)] 2 DL /r{[1/(2p¢50,000)r)] [1¢1012]} DL2/(r2 C x2)0.5 DL2/[r¢x/(r2 C x2)0.5] Arctan(x/r)180/p DL[arctan(x/r)180/p {[(r2 C x2)0.5] weigth}/DL2

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and


Xcm D Xci C a.Tm ¡ T /;

(4)

where Ri D calculated resistance at Ti (i.e., standardized temperature at i! C), a D slope of linear temperature model, and Tm D fish body temperature when resistance measurements are taken. Standardizing resistance and reactance values allow monitoring of a subject regardless of temperature differences. The use of this equation requires (1) a slope value for the change in resistance and reactance values with fish body temperature, (2) a noninvasive measurement of fish body temperature, and (3) a standardized temperature to which BIA measurements will be corrected. The BIA measurements in combination with this equation could reduce error and allow for BIA usage in both field and laboratory studies where fish body temperature is not controlled. Methods for establishing a temperature correction, however, have not been standardized. It is also important is to acknowledge any inherent variability in the BIA technique in temporal studies of individual fish; therefore, the precision of BIA measurements should be assessed. During consecutive measurements of BIA of an individual fish, the electrode location and distance between electrodes may be different from the preceding measurement. Although locations of electrodes are standardized (typically inserted posterior to the operculum and anterior to the adipose fin), the exact location and distance may vary by a few millimeters, which could result in different predicted lipid values. There is also a potential for bias caused by stress or injury after repeat measurements because reactance values can be influenced by lysed cells (Cox et al. 2011). Determining the variability between repeated measurements would indicate the reliability and precision of BIA. Currently, there are no studies showing the precision and inherent variability of BIA in individual fish. The purpose of this study was to assess temperature corrections and precision between repeated measurements using BIA in Atlantic Salmon Salmo salar parr. Our first objective was to determine whether a temperature correction equation can be applied to reduce the error in BIA estimates associated with temperature. A temperature correction was derived from a point-slope equation by using the linear relationship between BIA values and body temperature in wild Atlantic Salmon parr. Our second objective was to determine the precision of BIA by assessing the variability associated with repeated BIA measurements in individual salmon parr. The results from this study will determine the feasibility of using BIA to assess the lipid content of Atlantic Salmon parr in field studies. As part of our assessment of the BIA technique, we also investigated the potential effect of implanted body tags. Passive integrated transponder (PIT) tags are small glass encapsulated transponders, with an individual identification code, often implanted into the abdomen or muscle tissue of fish

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(e.g., Roussel et al. 2000). These tags may have an effect on BIA readings because of the high resistance value of glass, which could result in erroneous predictions of lipid content (Giancoli 1995). Therefore, a third objective of our study was to determine whether PIT tags surgically implanted into the body cavity of Atlantic Salmon parr can affect BIA measurements and their associated predicted lipid values.

METHODS Bioelectrical impedance analysis protocol.—The BIA measurements were performed using a Quantum III Bioelectrical Body Composition Analyzer (RJL Systems, Michigan) with four stainless, 28-gauge (0.32 mm) needle electrodes (Grass Telfactor, Rhode Island), as modified based on Cox and Hartman (2005), which were inserted in two locations. Electrodes were paired to have a detecting and sending electrode modified with a plastic sleeve filled with silicon (leaving 2 mm exposed) and mounted onto a plastic sheet, set 1.0 cm apart, for stabilization during measurements. We found this electrode depth was not appropriate for fish less than 50 mm FL because the electrodes could puncture the spine. Fish were anaesthetized with clove oil (40 mg/L) and placed on a nonconductive foam pad, and electrodes were inserted into two positions dorsal to the lateral line: (1) posterior to the operculum, and (2) anterior to the adipose fin (Figure 1). Detecting electrodes were positioned toward the medial portion of the fish, while the sending electrodes were positioned toward the head or tail. Resistance, reactance, and detector length were measured during BIA of each fish. The BIA values and detector length where then used to calculate a suite of electrical values to be used in the lipid models (Table 1; Cox and Hartman 2005; Hafs and Hartman 2011; Stolarski et al. 2014). Internal body temperature was also measured using a modified hypodermic thermocouple (Model HYP-1 K-type, Omega, Stamford, Connecticut) connected to a handheld digital thermometer (Model HH503, Omega, Stamford, Connecticut). The thermocouple diameter is similar to the BIA electrodes at 0.25 mm (electrode diameter D 0.32 mm) and was modified

FIGURE 1. Locations of bioelectrical impedance analysis electrodes on an Atlantic Salmon parr, where S represents signal sending electrode position, D represent receiving electrode position, and T represents thermocouple position.


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VUE ET AL.

in the same manner by attaching a plastic sleeve and filling the sleeve with silicon, leaving 2 mm of the thermocouple exposed. The thermocouple was inserted midway between the insertion points of the BIA electrodes above the lateral line (Figure 1) and was removed immediately prior to BIA readings to eliminate any changes in resistance and reactance values that may be caused by the thermocouple. Aside from our temperature study, fish body temperature was not controlled. All BIA measurements were collected in a wet laboratory immediately following without acclimating water temperature after collecting fish from an outdoor environment. Room temperature was set at comfortable working conditions (about 18! C). This setup replicates streamside conditions where air temperature and water temperature can differ. All experiments were conducted under the guidelines of the Canadian Council on Animal Care and the University of New Brunswick. Wild Atlantic Salmon parr (n D 130, 56–165 mm FL, 1.69– 43.65 g) were collected with a backpack electrofisher (LR-24, Smith-Root, Vancouver, Washington) from the Right Hand Branch of the Tobique River, Victoria County, New Brunswick, Canada (47! 140 2800 N, 67! 080 3000 W) during late September 2011. All parr were transferred to the Mactaquac Biodiversity Centre (Department of Fisheries and Oceans), Kingsclear, New Brunswick, and retained in large, artificial stream channels. Parr fed ad libitum on the macroinvertebrate community, seeded by the nearby St. John River (the water source for the tanks). The parr were allowed 1 month to acclimate to the stream-channels prior to experimentation. Temperature effects and correction.—Resistance and reactance values of euthanized fish were monitored as body temperature increased from 6! C to 14! C. This temperature range falls within the optimum temperature ranges in the field, (0–22! C; Elliott 1991). Atlantic Salmon parr (n D 13, November 11 2011) were euthanized using an overdose of clove oil and immediately placed into an ice bath to decrease body temperature. Once fish body temperature reached 6! C (about 2 min), the fish was immediately placed on a nonconductive foam pad at room temperature and connected to BIA electrodes. The fish were chilled within this short period to reduce the effect of rigor mortis on BIA values (Cox et al. 2011). Fish body temperature was allowed to gradually increase to room temperature. The BIA electrodes were set in fixed positions for every fish throughout the experiment to reduce any effects caused by different electrode locations. Internal body temperature was collected using a modified hypodermic thermocouple (described in the BIA protocol above) inserted midway between the insertion points of the BIA electrodes above the lateral line. The thermocouple was removed immediately prior to BIA readings to eliminate any changes in resistance and reactance values that could have been caused by the thermocouple. Resistance, reactance, and body temperature readings were recorded every 0.5! C until the body temperature reached 14! C (about 20 min).

We corrected resistance and reactance by estimating resistance and reactance at a standardized temperature (T12 D 12! C; i.e., the average body temperature of the fish used in the lipid model; range D 4.5! C to 20! C) using the average slope in the correction equation: R12 D br £ ðT12 ¡ Tm Þ C Rm

(5)

Xc12 D bxc £ ðT12 ¡ Tm Þ C Xcm :

(6)

and

Tm D measured fish body temperature using modified thermocouple prior to taking BIA measurements, Rm and Xcm D resistance and reactance measured at Tm , respectively, br and bxc D slope values in resistance and reactance with temperature, respectively, and R12 and Xc12 D calculated resistance and reactance at T12 , respectively. The corrected resistance and reactance values were then used to calculate corrected electrical values listed (Table 1). Lipid models.—To develop a predictive lipid model we compared lipid measurements derived via lipid extraction in the laboratory with electrical values from BIA. Following the completion of the temperature correction and precision experiment (described below), 54 Atlantic Salmon parr (56–147 mm FL) were euthanized and measured and weighed before undergoing BIA and measuring of body temperature with the thermocouple. Fish were stored in a ¡20! C freezer until lipid analysis. For lipid analysis, whole fish were oven dried at 60! C (until dry weight was consistent) and homogenized with a ball mill grinder. Lipid extraction was performed using the method described by Bligh and Dyer (1959), where lipids are extracted using a solution of chloroform and methanol. Total lipids (g) and percent lipids (total lipids/wet weight £ 100) were then calculated. Total lipids and percent lipids were separately modeled with biological and electrical variables (Table 1, uncorrected for temperature) and the best model was selected using multivariate ordinary least-squares regression (OLS) in the R package rms (Hafs and Hartman 2011; Harrell 2014; Stolarski et al. 2014). Subsets of the global model were created using the R package leaps and were arranged by the lowest Mallows’ Cp score (Lumley 2009). We selected the model with the lowest Mallows’ Cp score and validated the model with random subsets of the data set using the validate function within the R package rms. A temperature-corrected total lipid and percent lipid model was created using the variables selected in the uncorrected model. The uncorrected variables were replaced with corrected electrical values, and an ordinary least-squares regression was performed for the coefficients. We used R2 and root


NOTE

mean square error (RMSE) to compare the uncorrected and corrected model. Precision analysis.—Repeated BIA estimates of lipid content were used to evaluate the precision of BIA. Four time trials were conducted at 1 min, 1.5 h, 3 h, and 6 h intervals with 10 fish within each trial (56–115 mm FL, November 1,9 2011). During each time trial, seven repeated BIA measurements were collected for each individual fish. The 1 min trial was chosen to represent variation within the typical time needed to handle fish for BIA (8 g received 22-mm tags. Fish were tagged in September 2011 by creating a small incision between the pectoral girdle and inserting the tag posteriorly. The incision was closed using a single suture. Subsets of the parr were lethally sampled on November 9, 2011; December 9, 2011; December 28 2011; and January 25, 2012 after tagging (as per protocol for a different project). No visible wounds from surgery were found during or after first lethal sampling. Fish were euthanized with an overdose of clove oil, after which body temperature and BIA measurements were collected with the PIT tag remaining in the body cavity. An incision was made on the ventral portion of the fish and the PIT tag was removed. Fish body temperature and BIA measurements were then collected without PIT tags in the body cavity. Electrodes were repositioned during each measurement. Data analysis.—To determine whether changes in fish body temperature affect resistance and reactance values, we used a mixed model analysis of covariance (ANCOVA), where the

239

dependent variable was either the resistance or reactance value, the individual fish (FID) was the independent random variable, fish body temperature (FTEMP, fixed) was the covariate, and an interaction term (FID-FTEMP) was used to determine whether slopes of resistance or reactance values were similar. To assess whether changes in body temperature affect resistance or reactance values, we tested the null hypothesis that the fish body temperature does not affect resistance or reactance values. To determine whether slopes between different fish are similar, we tested the null hypothesis that the interaction term FID-FTEMP D 0 (parallel slopes). We also compared the slope values of resistance and reactance for each individual fish to the average temperature slope using a onesample t-test. We tested the null hypothesis that the individual slopes were equal to the average slope (a D 0.05). We also tested the feasibility of the temperature correction equation by comparing the changes in estimated total lipid content from BIA as temperatures increased between uncorrected and corrected reactance in parallel values. A mixed model ANCOVA was used to determine whether predicted lipid content changed with increased body temperature, where the covariate was body temperature (FTEMP, fixed), and the random independent variable was the individual fish (FID) and FID-temp interaction term was used to test for similar slopes. To assess whether PIT tags affected BIA readings we used a repeated measures analysis of variance (ANOVA) where the dependent variable was the predicted lipid content from BIA, and the independent variables were the presence of PIT tag (TAG, fixed), sample date, individual fish (FID, random; Girden 1992). Separate ANOVAs were performed for fish with the 12.5-mm and 22-mm tags. A repeated measures ANOVA was used to test whether predicted lipid content changed after repeated measurements within a time series, where the dependent variable was the predicted lipid content estimated from BIA, the independent variable was the individual fish (FID, random), the covariate was time (TIME, fixed) and the interaction term was FID-TIME. We tested the null hypothesis that there were no changes in lipid values between the different measurements for each time trial. The standard deviations between repeated measurements were then used to describe the precision of BIA (Barford 1985). All statistical tests assume a D 0.05 and were performed using R along with the R package nlme (Pinheiro et al. 2011; R Development Core Team 2011).

RESULTS Temperature Effects and Corrections Increases in fish body temperature resulted in linear decreases in resistance and reactance values, and slopes were not equal to zero (F1, 179 D 8,321, P < 0.001). Resistance decreased at an average rate of ¡14.41 V/! C (SE D 0.18),


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VUE ET AL.

whereas reactance decreased at a rate of ¡2.26 V/! C (SE D 0.049). We compared our average resistance and reactance slopes with those found in Cox et al. (2011) using a t-test. Average resistance slopes in our study were significantly greater than their slopes (P < 0.001, ¡11.65 V/! C, SE D 0.21); however, reactance slopes between our study and their study were not significantly different (P D 0.35, ¡2.92 V/! C, SE D 0.53). Resistance and reactance slopes varied between individual Atlantic Salmon parr (F1, 179, P < 0.0001 for both). Though resistance and reactance slopes were significantly different between fish, the standard deviations between slopes were small, resistance slopes ranging from ¡16.60 to ¡13.09 V/! C (SD D 1.31). Reactance slopes ranged from ¡3.35 to ¡1.70 V/! C (SD D 0.5). For resistance slopes, we found 2 of the 13 slopes were significantly different from the average slope (Table 2). For reactance slopes, 5 of the 13 slopes were significantly different from the average slope (Table 2).

TABLE 2. One-sample t-test, testing individual mean resistance (R) and reactance (Xc) slopes for individual Atlantic Salmon parr against the overall mean for all tested fish (a D 0.05; asterisks denote significance.). Individual mean slopes represent resistance and reactance changes with increased temperature.

Slope ¡16.815 ¡14.177 ¡13.477 ¡13.881 ¡10.147 ¡14.17 ¡14.773 ¡15.835 ¡14.468 ¡13.071 ¡13.09 ¡15.588 ¡14.487 ¡1.899 ¡2.157 ¡2.343 ¡2.531 ¡1.299 ¡1.955 ¡2.966 ¡2.468 ¡3.146 ¡2.013 ¡2.165 ¡3.188 ¡1.952

SE

t-value

Response D R; null m D ¡14.41 0.507 ¡4.748 0.719 0.324 0.782 1.193 0.883 0.599 1.007 4.234 0.672 0.356 0.747 ¡0.485 0.748 ¡1.905 0.885 ¡0.066 0.786 1.704 0.828 1.594 0.87 ¡1.355 0.661 ¡0.117 Response D Xc; null m D ¡2.26 0.125 2.888 0.177 0.583 0.193 ¡0.43 0.218 ¡1.246 0.248 3.869 0.166 1.842 0.184 ¡3.835 0.184 ¡1.128 0.218 ¡4.058 0.194 1.276 0.204 0.467 0.214 ¡4.325 0.163 1.891

P 0* 0.371 0.19 0.322 0.001* 0.368 0.347 0.069 0.39 0.095 0.112 0.155 0.391 0.011* 0.328 0.356 0.177 0.003* 0.076 0.002* 0.205 0.001* 0.172 0.349 0.001* 0.07

FIGURE 2. Bioelectrical impedance analysis models for (A) total lipids and (B) percent lipids as a function of fork length in Atlantic Salmon parr.

We corrected resistance and reactance to a standard temperature of 12! C using the average slope values of ¡14.41 for br and ¡2.26 for bxc in the correction equations (5) and (6). Lipid Models The total lipids (via lipid extraction) of the parr used in the model ranged from 0.054 to 0.53 g (Figure 2A). Percent fat ranged from 1.4% to 6.5% (Figure 2B). Best-fitting model coefficients for total and percent lipid models are described in Table 3. Our best uncorrected total lipids model was correlated with total lipids (r2 D 0.706, P < 0.001, RMSE D 0.1115; Figure 3A). However, the best percent lipid model (uncorrected) correlated poorly with the data set (R2 D 0.304, P < 0.001, RMSE D 1.05; Figure 3B). The corrected total lipid model also correlated with the data (R2 D 0.699, P < 0.001, RMSE D 0.1128; Figure 3A). For the uncorrected total lipid model RMSE was 1.15% higher than for the corrected model. Both models estimated identical average lipid content of 0.33 g. There was also no change in the percent lipid model performance after temperature corrections were added (R2 D 0.308, P < 0.001, RMSE D 1.05; Figure 3B). Using the uncorrected total lipid model, we found increases in fish body temperature decreased the predicted lipid value (Figure 4A). Slopes ranged from ¡0.0005 to ¡0.067 g for every 1! C increase, an average of ¡0.036 g/! C. We found an

FIGURE 3. Comparison between uncorrected and temperature-corrected models, as applied to Atlantic Salmon parr for (A) observed versus predicted total lipids, and (B) observed versus predicted percent lipids. For both lines the slope D 1 and the intercept D 0.

NOTE

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content after we corrected for temperature (R2 D 0.1, P D 0.308, Figure 5B).

FIGURE 4. A comparison between (A) uncorrected and (B) temperature-corrected total lipid predictions as a function of Atlantic salmon parr body temperature. Regression lines represent predicted total lipids from bioelectrical impedance analysis for individual fish.

8! C increase in fish body temperature decreased the predicted lipid content by an average of 0.27 g, representing a 55.2% change from the average initial lipid content of 0.50 g (SE D 0.14). We also found the predicted total lipids increases faster with temperature in larger fish than with smaller fish. There was a linear relationship between fork length and the rate predicted lipids with temperature (R2 D 0.79, P < 0.001, Figure 5A). The increase in predicted lipids in larger fish was probably related to increasing the detector length measurement [DL] within electrical calculations where the term is often squared. After temperature correcting BIA values, we found increasing body temperature by an average of 8! C only increased the predicted lipid content by an average of 0.012 g (SE D 0.0083), representing a 2.55% change from initial predictions. These changes are greatly lower than those found using the uncorrected model, where there was 55.2% change. Slopes ranged from ¡0.0006 to ¡0.007 g for every 1! C increase, with an average of 0.001 g/! C. We found the majority of the slopes were equal to zero; however, 3 of 13 fish were found to have slopes not equal to zero (Table 4). There was no linear relationship between length and the change in predicted lipid

FIGURE 5. Average increase in bioelectrical impedance analysis predicted lipids for individual Atlantic Salmon parr in relation to length for (A) uncorrected and (B) temperature-corrected models.

Precision Analysis The BIA estimates of lipid content were not significantly different among repeated measurements. During the 1 min trial, lipid estimates were not significantly different between the seven BIA readings (F6, 54 D 0.17, P D 0.983). The average standard deviation of predicted lipid content during the 1 min trial was 0.0015 g, representing a 0.64% change from the average predicted lipid content of 0.23 g. For the longer time trials (1.5, 3, and 6 h), there were also no significant differences in the predicted lipid content between the repeated measurements taken on an individual fish (F6, 54 D 0.90, P D 0.49 for 1.5-h trial; F6, 54 D 1.3, P D 0.23 for 3-h trial; and F6, 54 D 1.1, P D 0.39 for 6 h trial). However, there was greater variability in the predicted lipids for the longer time intervals, as evidenced by the higher average standard deviations: 0.015 for 1.5 h trial, 0.017 for 3 h, and 0.017 for 6 h. This variability represents an average of 6.43% change from the respective average lipid values of 0.26 g.

Effect of Implanted PIT Tags We found that PIT tags placed within the body cavity of Atlantic Salmon parr did not affect estimated lipid content when the BIA values were corrected for body temperature. For both the 12.5-mm and 22-mm size-classes, the repeated measures ANOVA showed no significant difference in estimated lipid content after removal (F1, 36 D 2.51, P D 0.13 and F1, 34 D 2.66, P D 0.11, respectively). These findings were consistent throughout all sampling events for both 12.5 and 22 mm tags (F3, 36 D 2.61, P D 0.067 and F3, 34 D 0.98, P D 0.41, respectively). On average, the estimated total lipids changed by only 3.44% (0.008 g, SE D 0.005) after the 12.5 mm PIT tags were removed. The average increase of estimated lipids was 2.89% (0.013 g, SE D 0.008) after the 22 mm tags were removed. Before temperature correcting the BIA data, we found estimated lipid content was significantly different after removing the 12.5-mm (F 1, 36 D 8.58, P D 0.005) and 22-mm (F1, 34 D 18.11, P < 0.001) PIT tags. These findings were also consistent throughout the sampling events for both 12.5-mm (F3,36 D 0.90, P D 0.45) and 22-mm (F3, 34 D 0.50, P D 0.68) tags. The process of handling and removing PIT tags from the body cavity, resulted in an increased body temperature, fish implanted with the 12.5-mm tags (